In March 2024, X's web page Twitter.com had *** billion website visits worldwide, up from *** billion site visits the previous month. Formerly known as Twitter, X is a microblogging and social networking service that allows most of its users to write short posts with a maximum of 280 characters.

In the six months ending March 2024, the United States accounted for 23.21 percent of web traffic to the Twitter.com URL. Japan ranked second, accounting for 16.06 percent of traffic to the web page, and was followed by the United Kingdom, representing 5.51 percent of the web address online volume.

Traffic analytics, rankings, and competitive metrics for twitter.com as of May 2025

These are the key Twitter user statistics that you need to know.

This is the breakdown of Twitter users by age group.

Advertising makes up 89% of its total revenue and data licensing makes up about 11%.

These Twitter user statistics will give you the complete story of where Twitter is at today and what the future looks like for the social media company.

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

Updated daily

London Base Station, Population, and Tweet DensityThe data shows the Tweet density, Base Station density, and Population density for each of the Greater London wards. A total of 532 wards are shown, with the following units: (1) Twitter data is over a 2 week period in 2012, (2) BS density is open data, and (3) Population density is residency data at 2011 census.traffic_bs_pop_wards_density.xlsx

Social network X/Twitter is particularly popular in the United States, and as of February 2025, the microblogging service had an audience reach of 103.9 million users in the country. Japan and the India were ranked second and third with more than 70 million and 25 million users respectively. Global Twitter usage As of the second quarter of 2021, X/Twitter had 206 million monetizable daily active users worldwide. The most-followed Twitter accounts include figures such as Elon Musk, Justin Bieber and former U.S. president Barack Obama. X/Twitter and politics X/Twitter has become an increasingly relevant tool in domestic and international politics. The platform has become a way to promote policies and interact with citizens and other officials, and most world leaders and foreign ministries have an official Twitter account. Former U.S. president Donald Trump used to be a prolific Twitter user before the platform permanently suspended his account in January 2021. During an August 2018 survey, 61 percent of respondents stated that Trump's use of Twitter as President of the United States was inappropriate.

This dataset includes a one percent sample of German-language Twitter retweets in Twitter raw data format. For each day, all retweets are stored in json data format (one entry per line).

The dataset was recorded using Tweepy and exported from a MongoDB database. It is intended to be imported into a MongoDB database to run analytical queries. It is not intended to be processed as is.

The dataset covers 60 consecutive days and ends on 01/25/2023.

The dataset was recorded as part of this study.

Kratzke, N. How to Find Orchestrated Trolls? A Case Study on Identifying Polarized Twitter Echo Chambers. Computers 2023, 12, 57. https://doi.org/10.3390/computers12030057

The US has historically been the target country for Twitter since its launch in 2006. This is the full breakdown of Twitter users by country.

QAP regressions for popular websites (Alexa)/ videos (YouTube)/ topics (Twitter) similarity across countries (Final block, September).

1 Classification Dataset

This dataset for the classification model contains 3,804 tweets, where 1,902 are related to traffic accident reports (TA, positive class) and 1,902 are unrelated (NTA, negative class).

For training the tweet classification model, a collaborative labeling strategy was designed. Here, 30 people labeled data according to the instructions given. Each participant had to evaluate a tweet to manually classify it into one of three categories defined as: traffic accident related, unrelated and don´t know/no response. Each tweet was evaluated by 3 participants. The correct label was selected by voting; the 3 people must agree on the selected label, otherwise the tweet was excluded from training. This process took a month and required the development and deployment of a web application.

2 NER Dataset (Named Entity Recognition)

For the entity recognition model training, a sample of the filtered tweets resulting from the previous classification phase was taken. 1,340 tweets were extracted, where 800 are from “unofficial” users, almost 60% of the sample. These tweets were user reports on traffic incident occurred in Bogota from October 2018 to July 2019, including other tweets that contained some location references such as reports on the state of road infrastructure; some tweets from the years 2016 and 2017 were also included. Although these posts were not related to accidents per se, they were selected because they contained location information. The purpose was to train a model that would recognize these entities, because a classifier of accident-related tweets was previously created. Additionally, the dataset was split, reserving 1,072 tweets for training and 268 for evaluation.

This dataset was manually labeled using the IOB (Inside-outside- beginning) format. The labeling tool called Brat Annotation Tools was used for this task. The labels defined are Location, which refers to the location of the report; and Time, which refers to the time or date of the incident. Accordingly, 5 labels were generated: B-loc, I-loc, B-time, I-time and O. The O label refers to Others.

3 Traffic accident Twitter geolocation

A dataset with 26362 traffic accident tweets with the coordinates of the incident and the date of publication.

Modeled probability of congestion for selected cities based on Twitter and OpenStreetMap data on a grid cell basis with a resolution of 100 meters. The data set includes the cities of Barcelona, ​​Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo and Seattle. The range of values ​​is from 0 (probably normal traffic flow) to 1 (high probability of traffic flow delay). Methodology: Based on Twitter and OpenStreetMap (OSM) data, a model was trained with the help of machine learning, which predicts the probability of traffic jams within the cities. Publicly provided data from UBER was used as reference data (https://movement.uber.com). The number of tweets and the number of points of interest from OSM near roads were used as indicators in the model. In addition, car journeys were simulated with the help of the openrouteservice based on the spatial distribution of the population and relevant POIs and taken into account in the model.

The platform is male-dominated with 68.1% of all Twitter users being male. Just 31.9% of Twitter users are female.

The number of Twitter users in France was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Luxembourg and Netherlands.

While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.

The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.

Instructions

Data have been exported in three formats to provide the maximum flexibility - MongoDB Dump BSONs: To import these data, please refer to the official MongoDB documentation. - JSON Exports: Both the users and the tweets collections have been exported as canonical JSON files. - CSV Exports (only tweets): The tweet collection has been exported as plain CSV file with comma separators.

Preliminary research efforts regarding Social Media Platforms and their contribution to website traffic in LAMs. Through the Similar Web API, the leading social networks (Facebook, Twitter, Youtube, Instagram, Reddit, Pinterest, LinkedIn) that drove traffic to each one of the 220 cases in our dataset were identified and analyzed in the first sheet. Aggregated results proved that Facebook platform was responsible for 46.1% of social traffic (second sheet).